Titlebook: Machine Learning and Data Mining in Pattern Recognition; 12th International C Petra Perner Conference proceedings 2016 Springer Internation

neuron

AdaMS: Adaptive Mountain Silhouette Extraction from Images,eous parts in the silhouette and show how our algorithm uses this information to recalculate the silhouette in the surroundings of the error. We also show that our method yields good results by evaluating our approach on an existing data set of mountain images.

現(xiàn)任者

A Time Series Model of the Writing Process,s is advanced. An empirical distribution over the whole document of this feature specifies the writing style. So, dissimilarity of such distributions indicates a difference in the writing styles, and their coincidence implies the styles’ identity. Numerical experiments demonstrate high potential ability of the proposed approach.

Lament

Semantic Aware Bayesian Network Model for Actionable Knowledge Discovery in Linked Data, it not only accomodates the sematnic aspects in LD, but also caters to the need of connectign different data-sets from different domains. We evaluate the proposed model on a Bone Dysplasia dataset, Experimental results show promising perfomance.

人工制品

Driving Style Identification with Unsupervised Learning,teristics). Note as a distinguished particular feature of the presented method: it does not require availability of the training labels. The database includes 2736 drivers with 200 variable length driving trajectories each. We tested our model (with competitive results) online during Kaggle-based AXA Drivers Telematics Challenge in 2015.

健忘癥

DEFT

Conference proceedings 2016The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining..

卵石

EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets,ne non-closed high-utility itemsets. Furthermore, it also introduces novel utility upper-bounds and a transaction merging mechanism. Experimental results shows that EFIM-Closed can be more than an order of magnitude faster and consumes more than an order of magnitude less memory than the previous state-of-art CHUD algorithm.

出處

責(zé)問

Conference proceedings 20162016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the d

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Robert E. Marmelstein,Alexander L. Hunt,Christoper Eroh